PSO based Neural Networks vs. traditional statistical models…
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- DOI 10.1109/iadcc.2013.6514315
- OpenAlex ID W2047040915
- Type Conference-paper
- Language EN
- Published 01 Feb 2013
- Pages 719–725
- Source IEEE International Advance Computing Con…
- FWCI 5.07
- Countries 1
- Institutions 2
- Added 24 Jun 2016
- Updated 16 Oct 2026
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PSO based Neural Networks vs. traditional statistical models for seasonal time series forecasting
Conference-paper 2013 EN Green
Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for…
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3 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | R. Adhikari | Jawaharlal Nehru University (IN) | — |
| 2 | R. K. Agrawal | Jawaharlal Nehru University (IN) | — |
| 3 | L. Kant | Indian Institute of Technology… (IN) | — |
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